Since Gregor Mendel first elucidated the principles of genetics and inheritance, the experimental basis for understanding heritable traits, like susceptibility to disease has largely involved studying biological systems one component at a time. However, the one-at-a-time approach does not scale when faced with a genome consisting of -30,000 genes as occurs in humans and other mammals. This limitation is further compounded by the complex interaction between genes and the environment. Factorial experimentation, described in the 1930's by Sir Ronald Fisher, in which many or all components of a system are altered simultaneously through randomization, is far more efficient. Analyses of all genes concurrently will be essential if we are to imagine a time when we understand human (mammalian) biology sufficiently well to synthetically reassemble biological knowledge to accurately predict and intelligently alter traits with complex etiologies, like susceptibility to common diseases. With the realization that a new model population was needed to understand human diseases with complex etiologies, we designed the Collaborative Cross (CC). The CC provides a translational tool to integrate gene functional studies into genetic networks and variation maps of the biomolecular space, containing all the biomolecules between the primary DMAsequence and the terminal disease phenotypes of interest, using a realistic population structure, which will be essential to understand the intricacies of biological processes like altered disease susceptibility. The CC is a large, highly innovative panel of recombinant inbred (Rl)lines of mice that combines the genomes of eight genetically diverse founder strains - A/J, C57BL/6J, 12981/SvlmJ, NOD/LtJ, NZO/HILU, CAST/EiJ, PWK/PhJ, and WSB/EiJ - to capture almost 90%of the known variation present in laboratory mice. To achieve the goal of producing the CC, we have formed the Systems Genetics Resource Consortium that will 1) develop the CC and assocaited husbandry and phenotype databases, 2) genotype the CC during development to identify synthetic lethal events and for quality control, and 3) develop a web portal for the CC for community access to data and analytic programs.